Forecasting of future greenhouse gas emissions trajectory for India using energy and economic indexes with various metaheuristic algorithms

Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions

Improving the efficiency with which natural resources are utilized is an indispensable for achieving sustainable development and carbon neutrality. By analyzing the utilization efficiency of energy, water, and land, we established a comprehensive natural resource utilization efficiency index (NRUEI). We then calculated the 2016 index for 165 cities in China, and investigated what caused it to vary. We found three main results: (1) the NRUEI varied greatly across China and there were significant positive correlations between urban energy utilization efficiency (EUE), water utilization efficiency (WUE) and land utilization efficiency (LUE); (2) the NRUEI showed a significant positive correlation with a city's population size, economy (Gross Domestic Product (GDP)), and the level of urban social development (GDP per capita); (3) cities in East China have the highest natural resource utilization efficiency, while cities in Northeast China have the lowest. These results indicate that China's increasing urban development is associated with rising natural resource utilization efficiency and that the city's endowment of natural resources is an important factor affecting that efficiency. Further, the results showed that the determinants of a city's NRUEI differed in large and small cities. Lastly, our results suggest that improving EUE is key for improving NRUEI in urban China, and different efficiencies can be improved intertwined. A major takeaway of this study is that there is great potential for improving natural resource utilization efficiency in Chinese cities and we include city-specific suggestions for efficiency improvements. 

Urban development and resource endowments shape natural resource utilization efficiency in Chinese cities.

Analyzing the Role of High-Tech Industrial Agglomeration in Green Transformation and Upgrading of Manufacturing Industry: the Case of China

Role of greener energies, high tech-industries and financial expansion for ecological footprints: Implications from sustainable development perspective

The rapid development of China’s economy has largely relied on energy consumption, which has caused serious air pollution and affected public health, and economic development, energy consumption, air pollution, and public health have nowadays become the focus of academic attention. However, the previous literature failed to consider undesirable output when constructing the Dynamic Network DEA model to study the efficiencies of energy consumption, air pollution, and public health. As a result, past studies did not employ those three issues in a structure to effectively reflect and solve the problems. Therefore, this paper constructs the Two-Stage Dynamic Undesirable DEA model and puts energy consumption, air pollution, and public health into the same framework in order to fill the gap in the literature. Findings show that the production consumption efficiency stage is better than the health protection stage, and that the efficiency values of variables vary significantly in different regions. The efficiency of tumor and tuberculosis is the lowest, with oil consumption and birthrate efficiencies are the best, followed by coal, nitrogen oxide (NOx), and dust efficiencies. Coal efficiency exhibits a fluctuating downward trend, whereas the efficiencies of electricity, air pollutants, tuberculosis, and tumor tend to fluctuate upwards during the research period. In consideration of the varying performances of different regions in the two stages, we put forward suggestions based on these findings to improve the efficiencies of energy, environment, and public health in China.

Energy consumption, air pollution, and public health in China: based on the Two-Stage Dynamic Undesirable DEA model

Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.

https://www.mdpi.com/2071-1050/12/8/3284/pdf

Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

This paper proposes a novel regional carbon emission inequality (RCI) index based on a special kind of general distribution. Using the proposed RCI index and based on China’s county-level panel data over the time span of 1997–2017, the regional carbon emission inequality of China is evaluated at intra-provincial, sub-national, and national levels. Based on that, the dependence between regional carbon inequality and carbon efficiency is studied by using copula functions and nonlinear dependence measures. The empirical results show that: (1) Shanghai, Tianjin, and Inner Mongolia have the worst carbon inequalities; while Hainan, Qinghai, and Jiangxi are the three most carbon-equal provinces; (2) there is a divergence phenomenon in RCI values of municipalities over the past decade; (3) from the national-level perspective, the inter-provincial carbon emission inequality is much greater than that at the intra-provincial level; (4) from the sub-national-level perspective, the east region has the highest RCI value, followed by the northeast, west, and the central regions; (5) there is a so-called "efficiency–equality (E-E) trade-off" in each provincial administrative unit, meaning that the higher carbon efficiency generally comes with higher carbon inequality, i.e., carbon efficiency comes at a price of carbon inequality; and (6) by re-grouping provincial units via the efficiency–equality cost and industrial structure, respectively, both carbon equality and carbon efficiency can be achieved in some regions simultaneously, thereby getting out of the “E-E trade-off” dilemma. The empirical evidence may provide valuable insight regarding the topic of “equality and efficiency” in environmental economics, and offer policy implications for regional economic planning and coordination.

/pdf/evaluating-regional-carbon-inequality-and-its-dependence-1awv70jt.pdf

Evaluating Regional Carbon Inequality and Its Dependence with Carbon Efficiency: Implications for Carbon Neutrality

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Papers

Energy consumption, air pollution, and public health in China: based on the Two-Stage Dynamic Undesirable DEA model

Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

Evaluating Regional Carbon Inequality and Its Dependence with Carbon Efficiency: Implications for Carbon Neutrality